AI 中文总结
本研究评估了七个前沿LLM智能体选择生物工具及与EVEscape和ESM3互动的能力，识别了自主操作的技术障碍，为生物安全风险评估奠定基础。
AI 中文摘要
用于生物学研究的计算工具（生物工具，简称BT）在数量和被证明的能力方面都在快速进步。虽然这些技术为科学进步带来了诸多益处，但恶意行为者可能试图利用它们来设计和开发生物武器（BW）。通常，BT需要专业知识才能成功操作，这为非专家进行恶意使用设置了障碍。大语言模型（LLM）智能体可能通过使领域专业知识有限的行为者能够成功使用BT来削弱这一障碍。关于AI如何改变生物武器开发路径的评估应考察LLM智能体在相关生物设计任务中如何与BT互动，但此类评估目前有限。在本报告中，我们呈现了对LLM智能体在BT互动早期阶段能力的评估。首先，我们评估了七个前沿LLM智能体为给定任务选择合适BT的能力。其次，我们通过有针对性的案例研究评估这些LLM智能体如何与两个BT（EVEscape和ESM3）互动。这些案例研究探究了自主BT操作所需的使能能力，这是通往广泛风险路径的潜在切入点。我们识别并评估了LLM智能体与BT交叉领域的技术障碍，为生物安全社区和AI开发者提供了随着技术发展进行进一步风险和能力评估的基础。
英文摘要
Computational tools for biological research (biological tools, or BTs) are advancing rapidly in both quantity and demonstrated capability. While these technologies offer many benefits for scientific advancement, malicious actors may seek to exploit them to design and develop biological weapons (BWs). Generally, BTs require specialized expertise to operate successfully, presenting a barrier to nefarious use by nonexperts. Large language model (LLM) Agents may erode this barrier by enabling actors with limited domain expertise to successfully use BTs. Assessments of how AI could change bioweapons development pathways should examine how LLM Agents interact with BTs on relevant biological design tasks, but such assessments are limited. In this report, we present an assessment of the abilities of LLM Agents in the early stages of interaction with BTs. First, we evaluate seven frontier LLM Agents on their ability to select appropriate BTs for a given task. Second, we assess how these LLM Agents engage with two BTs, EVEscape and ESM3, through targeted case studies. These case studies probe the enabling capabilities required for autonomous BT operation, a potential entry point to a wide range of risk pathways. We identify and assess technical barriers at the intersection of LLM Agents and BTs, offering the biosecurity community and AI developers a foundation for further risk and capability assessments as technologies progress.
Comments78 pages, 16 figures
